This package allows calling the Leiden algorithm for clustering on an igraph object from R. See the Python and Java implementations for more details:
https://github.com/CWTSLeiden/networkanalysis
https://github.com/vtraag/leidenalg
This package requires the 'leidenalg' and 'igraph' modules for python (2) to be installed on your system. For example:
pip install leidenalg numpy python-igraph
Note you may need to uninstall the igraph 0.1.11 (now deprecated to jgraph) and install python-igraph or igraph-0.7.0:
pip uninstall igraph
pip install leidenalg python-igraph
The python version can be installed with pip or conda:
pip uninstall -y igraph
pip install -U -q leidenalg python-igraph
conda install -c vtraag leidenalg
It is also possible to install the python dependencies with reticulate in R.
library("reticulate")
py_install("python-igraph")
py_install("leidenalg", forge = TRUE)
If you do not have root access, you can use pip install --user or pip install --prefix to install these in your user directory (which you have write permissions for) and ensure that this directory is in your PATH so that Python can find it.
Dependancies can also be installed from a conda repository. This is recommended for Windows users:
conda -c vtraag python-igraph leidenalg
The stable 'leiden' package and the dependancies can be installed from CRAN:
install.packages("leiden")The 'devtools' package can also be used to install development version of 'leiden' and the dependancies (igraph and reticulate) from GitHub:
if (!requireNamespace("devtools"))
install.packages("devtools")
devtools::install_github("TomKellyGenetics/leiden", ref = "master")To use or test the development version, install the "dev" branch from GitHub.
if (!requireNamespace("devtools"))
install.packages("devtools")
devtools::install_github("TomKellyGenetics/leiden", ref = "dev")Please submit pull requests to the "dev" branch. This can be downloaded to your system with:
git clone --branch dev [email protected]:TomKellyGenetics/leiden.git
This package provides a function to perform clustering with the Leiden algorithm:
partition <- leiden(adjacency_matrix)For an igraph object 'graph' in R:
adjacency_matrix <- igraph::as_adjacency_matrix(graph)
partition <- leiden(adjacency_matrix)Calling leiden directly on a graph object is also avaible:
partition <- leiden(graph_object)See the benchmarking vignette on details of performance.
To generate an adjacency matrix from a dataset, we can compute the shared nearest neighbours (SNN) from the data. For example, for a dataset data_mat with n features (rows) by m samples or cells (columns), we generate an adjacency matrix of nearest neighbours between samples.
library(RANN)
snn <- RANN::nn2(t(data_mat), k=30)$nn.idx
adjacency_matrix <- matrix(0L, ncol(data_mat), ncol(data_mat))
rownames(adjacency_matrix) <- colnames(adjacency_matrix) <- colnames(data_mat)
for(ii in 1:ncol(data_mat)) {
adjacency_matrix[i,colnames(data_mat)[snn[ii,]]] <- 1L
}
#check that rows add to k
sum(adjacency_matrix[1,]) == 30
table(apply(adjacency_matrix, 1, sum))For a dimension reduction embedding of m samples (rows) by n dimensions (columns):
library(RANN)
snn <- RANN::nn2(embedding, k=30)$nn.idx
adjacency_matrix <- matrix(0L, nrow(embedding), nrow(embedding))
rownames(adjacency_matrix) <- colnames(adjacency_matrix) <- colnames(data_mat)
for(ii in 1:nrow(embedding)) {
adjacency_matrix[ii,rownames(data_mat)[snn[ii,]]] <- 1L
}
#check that rows add to k
sum(adjacency_matrix[1,]) == 30
table(apply(adjacency_matrix, 1, sum))This is compatible with PCA, tSNE, or UMAP results.
To use Leiden with the Seurat pipeline for a Seurat Object object that has an SNN computed (for example with Seurat::FindClusters with save.SNN = TRUE). This will compute the Leiden clusters and add them to the Seurat Object Class.
library("Seurat")
FindClusters(pbmc_small)
adjacency_matrix <- as.matrix(pbmc_small@snn)
partition <- leiden(adjacency_matrix)
pbmc_small@ident <- as.factor(partition)
names(test@ident) <- rownames(test@meta.data)
pbmc_small@meta.data$ident <- as.factor(partition)Suerat objects contain an SNN graph that can be passed directly to the igraph method. For example
library("Seurat")
FindClusters(pbmc_small)
membership <- leiden(pbmc_small@snn)
table(membership)
pbmc_small@ident <- as.factor(membership)
names(pbmc_small@ident) <- rownames(pbmc_small@meta.data)
pbmc_small@meta.data$ident <- as.factor(membership)library("RColorBrewer")
colourPal <- function(groups) colorRampPalette(brewer.pal(min(length(names(table(groups))), 11), "Set3"))(length(names(table(groups))))
pbmc_small <- RunPCA(object = pbmc_small, do.print = TRUE, pcs.print = 1:5, genes.print = 5)
PCAPlot(object = pbmc_small, colors.use = colourPal(pbmc_small@ident), group.by = "ident")
pbmc_small <- RunTSNE(object = pbmc_small, dims.use = 1:20, do.fast = TRUE, dim.embed = 2)
TSNEPlot(object = pbmc_small, colors.use = colourPal(pbmc_small@ident), group.by = "ident")
pbmc_small <- RunUMAP(object = pbmc_small, dims.use = 1:20, metric = "correlation", max.dim = 2)
DimPlot(pbmc_small, reduction.use = "umap", colors.use = colourPal(pbmc_small@ident), group.by = "ident")Note that this code is designed for Seurat version 2 releases. For Seurat version 3 objects, the Leiden algorithm will be implemented in the Seurat version 3 package with Seurat::FindClusters and algorithm = "leiden").
library("Seurat")
FindClusters(pbmc_small, algorithm = 4)These clusters can then be plotted with:
library("RColorBrewer")
colourPal <- function(groups) colorRampPalette(brewer.pal(min(length(names(table(groups))), 11), "Set3"))(length(names(table(groups))))
PCAPlot(object = pbmc_small, colors.use = colourPal(pbmc_small@active.ident), group.by = "ident")
TSNEPlot(object = pbmc_small, colors.use = colourPal(pbmc_small@active.ident), group.by = "ident")
pbmc_small <- RunUMAP(object = pbmc_small, reduction.use = "pca", dims.use = 1:20, metric = "correlation", max.dim = 2)
DimPlot(pbmc_small, reduction.use = "umap", colors.use = colourPal(pbmc_small@active.ident), group.by = "ident")#generate example data
adjacency_matrix <- rbind(cbind(matrix(round(rbinom(4000, 1, 0.8)), 20, 20), matrix(round(rbinom(4000, 1, 0.3)), 20, 20), matrix(round(rbinom(400, 1, 0.1)), 20, 20)),
##' cbind(matrix(round(rbinom(400, 1, 0.3)), 20, 20), matrix(round(rbinom(400, 1, 0.8)), 20, 20), matrix(round(rbinom(4000, 1, 0.2)), 20, 20)),
##' cbind(matrix(round(rbinom(400, 1, 0.3)), 20, 20), matrix(round(rbinom(4000, 1, 0.1)), 20, 20), matrix(round(rbinom(4000, 1, 0.9)), 20, 20)))
library("igraph")
rownames(adjacency_matrix) <- 1:60
colnames(adjacency_matrix) <- 1:60
graph_object <- graph_from_adjacency_matrix(adjacency_matrix, mode = "directed")
#plot graph structure
library("devtools")
install_github("TomKellyGenetics/igraph.extensions")
library("plot.igraph")
plot_directed(graph_object, cex.arrow = 0.3, col.arrow = "grey50")
#generate partitions
partition <- leiden(adjacency_matrix)
table(partition)
#plot results
library("RColorBrewer")
node.cols <- brewer.pal(max(partition),"Pastel1")[partition]
plot_directed(graph_object, cex.arrow = 0.3, col.arrow = "grey50", fill.node = node.cols)
For more details see the follow vignettes:
- running leiden on an adjacency matrix
https://github.com/TomKellyGenetics/leiden/blob/master/vignettes/run_leiden.html
- running leiden on an igraph object
https://github.com/TomKellyGenetics/leiden/blob/master/vignettes/run_igraph.html
- comparing running leiden in Python to various methods in R
https://github.com/TomKellyGenetics/leiden/blob/master/vignettes/benchmarking.html
Please cite this implementation R in if you use it:
To cite the leiden package in publications use:
S. Thomas Kelly (2020). leiden: R implementation of the Leiden algorithm. R
package version 0.3.3 https://github.com/TomKellyGenetics/leiden
A BibTeX entry for LaTeX users is
@Manual{,
title = {leiden: R implementation of the Leiden algorithm},
author = {S. Thomas Kelly},
year = {2020},
note = {R package version 0.3.3},
url = {https://github.com/TomKellyGenetics/leiden},
}
Please also cite the original publication of this algorithm.
Traag, V.A., Waltman. L., Van Eck, N.-J. (2018). From Louvain to
Leiden: guaranteeing well-connected communities.
`arXiv:1810.08473 <https://arxiv.org/abs/1810.08473>`
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